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/// ------------------------------------------------------
/// SwarmOps - Numeric and heuristic optimization for C#
/// Copyright (C) 2003-2011 Magnus Erik Hvass Pedersen.
/// Please see the file license.txt for license details.
/// SwarmOps on the internet: http://www.Hvass-Labs.org/
/// ------------------------------------------------------
using System.Diagnostics;
namespace SwarmOps.Optimizers.Parallel
{
/// <summary>
/// Parallel version of DE which computes the fitness of its agents
/// in parallel. Assumes the fitness function is thread-safe. Should
/// only be used with very time-consuming optimization problems otherwise
/// basic DE will execute faster because of less overhead.
/// </summary>
public class DE : Optimizer
{
#region Constructors.
/// <summary>
/// Construct the object.
/// </summary>
public DE()
: this(1)
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="problem">Problem to optimize.</param>
public DE(Problem problem)
: this(1, problem)
{
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="numAgentsMultiple">Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, ...</param>
public DE(int numAgentsMultiple)
: base()
{
NumAgentsMultiple = numAgentsMultiple;
}
/// <summary>
/// Construct the object.
/// </summary>
/// <param name="numAgentsMultiple">Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, etc.</param>
/// <param name="problem">Problem to optimize.</param>
public DE(int numAgentsMultiple, Problem problem)
: base(problem)
{
NumAgentsMultiple = numAgentsMultiple;
}
#endregion
#region Sets of control parameters.
/// <summary>
/// Control parameters.
/// </summary>
public struct Parameters
{
/// <summary>
/// Control parameters tuned for all benchmark problems in
/// 5 dimensions and 10000 fitness evaluations in one optimization run.
/// </summary>
public static readonly double[] AllBenchmarks5Dim10000Iter = { 32.0, 0.4845, 0.9833 };
/// <summary>
/// Control parameters tuned for all benchmark problems in
/// 30 dimensions and 60000 fitness evaluations in one optimization run.
/// </summary>
public static readonly double[] AllBenchmarks30Dim60000Iter = { 32.0, 0.3176, 0.5543 };
}
#endregion
#region Get individual control parameters.
/// <summary>
/// Population size multiple, e.g. 4 ensures populations are sized 4, 8, 12, 16, etc.
/// </summary>
public int NumAgentsMultiple
{
get;
protected set;
}
/// <summary>
/// Get parameter, Number of agents, aka. population size.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public int GetNumAgents(double[] parameters)
{
int numAgents = (int)System.Math.Round(parameters[0], System.MidpointRounding.AwayFromZero);
// Ensure numAgents falls on desired multiple.
numAgents--;
int mod = numAgents % NumAgentsMultiple;
numAgents += NumAgentsMultiple - mod;
return numAgents;
}
/// <summary>
/// Get parameter, CR, aka. crossover probability.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public double GetCR(double[] parameters)
{
return parameters[1];
}
/// <summary>
/// Get parameter, F, aka. differential weight.
/// </summary>
/// <param name="parameters">Optimizer parameters.</param>
public double GetF(double[] parameters)
{
return parameters[2];
}
#endregion
#region Base-class overrides, Problem.
/// <summary>
/// Name of the optimizer.
/// </summary>
public override string Name
{
get { return "DE-Simple-Par" + NumAgentsMultiple; }
}
/// <summary>
/// Number of control parameters for optimizer.
/// </summary>
public override int Dimensionality
{
get { return 3; }
}
string[] _parameterName = { "NP", "CR", "F" };
/// <summary>
/// Control parameter names.
/// </summary>
public override string[] ParameterName
{
get { return _parameterName; }
}
/// <summary>
/// Default control parameters.
/// </summary>
public override double[] DefaultParameters
{
get { return Parameters.AllBenchmarks30Dim60000Iter; }
}
static readonly double[] _lowerBound = { 3, 0, 0 };
/// <summary>
/// Lower search-space boundary for control parameters.
/// </summary>
public override double[] LowerBound
{
get { return _lowerBound; }
}
static readonly double[] _upperBound = { 200, 1, 2.0 };
/// <summary>
/// Upper search-space boundary for control parameters.
/// </summary>
public override double[] UpperBound
{
get { return _upperBound; }
}
#endregion
#region Base-class overrides, Optimizer.
/// <summary>
/// Perform one optimization run and return the best found solution.
/// </summary>
/// <param name="parameters">Control parameters for the optimizer.</param>
public override Result Optimize(double[] parameters)
{
Debug.Assert(parameters != null && parameters.Length == Dimensionality);
// Signal beginning of optimization run.
Problem.BeginOptimizationRun();
// Retrieve parameters specific to DE method.
int numAgents = GetNumAgents(parameters);
double CR = GetCR(parameters);
double F = GetF(parameters);
Debug.Assert(numAgents > 0);
// Get problem-context.
double[] lowerBound = Problem.LowerBound;
double[] upperBound = Problem.UpperBound;
double[] lowerInit = Problem.LowerInit;
double[] upperInit = Problem.UpperInit;
int n = Problem.Dimensionality;
// Allocate agent positions, fitness and feasibility arrays.
double[][] agentsX = Tools.NewMatrix(numAgents, n);
double[][] agentsY = Tools.NewMatrix(numAgents, n);
double[] fitnessX = new double[numAgents];
double[] fitnessY = new double[numAgents];
bool[] feasibleX = new bool[numAgents];
bool[] feasibleY = new bool[numAgents];
// Iteration variables.
int i, j;
// Fitness variables.
double[] g = null;
double gFitness = Problem.MaxFitness;
bool gFeasible = false;
// Initialize agent-position in search-space. (Non-parallel)
for (j = 0; j < numAgents; j++)
{
// Initialize position.
Tools.InitializeUniform(ref agentsX[j], lowerInit, upperInit);
// Enforce constraints and evaluate feasibility.
feasibleX[j] = Problem.EnforceConstraints(ref agentsX[j]);
}
// Compute fitness of initial positions. (Parallel)
// This counts as iterations below.
System.Threading.Tasks.Parallel.For(0, numAgents, Globals.ParallelOptions, (jPar) =>
{
fitnessX[jPar] = Problem.Fitness(agentsX[jPar], feasibleX[jPar]);
});
// Update best-found position. (Non-parallel)
for (j = 0; j < numAgents; j++)
{
if (Tools.BetterFeasibleFitness(gFeasible, feasibleX[j], gFitness, fitnessX[j]))
{
g = agentsX[j];
gFitness = fitnessX[j];
gFeasible = feasibleX[j];
}
// Trace fitness of best found solution.
Trace(j, gFitness, gFeasible);
}
// Perform optimization.
for (i = numAgents; Problem.Continue(i, gFitness, gFeasible); )
{
// Compute potential new position. (Non-parallel)
for (j=0; j<numAgents; j++)
{
// Refer to the j'th agent as x.
double[] x = agentsX[j];
// Refer to its potentially new position as y.
double[] y = agentsY[j];
// Pick a random dimension.
int R = Globals.Random.Index(n);
// Pick random and distinct agent-indices.
// Not necessarily distinct from x though.
int R1, R2;
Globals.Random.Index2(numAgents, out R1, out R2);
// Refer to the randomly picked agents as a and b.
double[] a = agentsX[R1];
double[] b = agentsX[R2];
// Compute potentially new position.
for (int k = 0; k < n; k++)
{
if (k == R || Globals.Random.Uniform() < CR)
{
y[k] = g[k] + F * (a[k] - b[k]);
}
else
{
y[k] = x[k];
}
}
}
// Compute fitness of y-position. (Parallel)
System.Threading.Tasks.Parallel.For(0, numAgents, Globals.ParallelOptions, (jPar) =>
{
// Enforce constraints and evaluate feasibility.
feasibleY[jPar] = Problem.EnforceConstraints(ref agentsY[jPar]);
// Compute fitness if feasibility (constraint satisfaction) is same or better.
if (Tools.BetterFeasible(feasibleX[jPar], feasibleY[jPar]))
{
fitnessY[jPar] = Problem.Fitness(agentsY[jPar], fitnessX[jPar], feasibleX[jPar], feasibleY[jPar]);
}
});
// Update agent-positions if improved fitness. (Non-parallel)
for (j = 0; j < numAgents; j++, i++)
{
// Update agent in case feasibility is same or better and fitness is improvement.
if (Tools.BetterFeasibleFitness(feasibleX[j], feasibleY[j], fitnessX[j], fitnessY[j]))
{
// Update agent's position.
agentsY[j].CopyTo(agentsX[j], 0);
// Update agent's fitness.
fitnessX[j] = fitnessY[j];
// Update agent's feasibility.
feasibleX[j] = feasibleY[j];
// Update swarm's best known position.
if (Tools.BetterFeasibleFitness(gFeasible, feasibleX[j], gFitness, fitnessX[j]))
{
g = agentsX[j];
gFitness = fitnessX[j];
gFeasible = feasibleX[j];
}
}
// Trace fitness of best found solution.
Trace(i, gFitness, gFeasible);
}
}
// Signal end of optimization run.
Problem.EndOptimizationRun();
// Return best-found solution and fitness.
return new Result(g, gFitness, gFeasible, i);
}
#endregion
}
} |